A Problem in Thermodynamics and Statistical Mechanics: Analytical and Numerical Study of an Einstein Solid

Every physics major at some point in their undergraduate career takes a course in thermodynamics and statistical mechanics. One of my problem sets included a problem that considers an Einstein solid with 50 oscillators and 100 units of energy and then increases the number of oscillators to 5000. I will be presenting my solution to the numerical side of the problem. An Einstein solid can be regarded as

“… a collection of microscopic systems which can store any number of energy ‘units’ of equal size which occur for any quantum-mechanical harmonic oscillator whose potential energy function has the form \displaystyle \frac{1}{2}k_{s}x^{2}…The model of a solid as a collection of identical oscillators with quantized energy units…”

described (defined) by Schroeder in his text Introduction to Thermal Physics. Figure 1 represents the Einstein solid as a whole (in a lattice) and Figure 2 depicts the quantum-mechanical harmonic oscillator interpretation of an Einstein solid.

The problem statement is:

“Use a computer to study the entropy, temperature, and heat capacity of an Einstein solid, as follows. Let the solid contain 50 oscillators (initially), and from 0 to 100 units of energy. Make a table, analogous to Table 3.2, in which each row represents a different value for the energy…Make a graph of entropy vs. energy, and a graph of the heat capacity vs. temperature. Then change the number of oscillators to 5000, and again make a graph of the heat capacity and temperature and entropy and energy, and discuss the predictions and compare it to the predictions to the data for lead, aluminum, and diamond. Estimate the numerical value of \displaystyle \epsilon for each of those solids.”

This problem can be found in the aforementioned text.




Figure 1. Einstein Solid (Lattice); Image Credit/Obtained from https://mappingignorance.org/2015/12/17/einstein-and-quantum-solids/


Figure 2. Quantum-Mechanical Harmonic Oscillator interpretation of an Einstein solid as a collection of these oscillators. Image Credit: http://hyperphysics.phy-astr.gsu.edu/hbase/Therm/einsol.html

Part I: Let q = 100 units, and let $N = 50$. The corresponding data table for this Einstein solid follows. The following set of equations were used to determine the multiplicity and entropy.

\displaystyle \Omega(N,q) = {q+N-1 \choose q} = \frac{(q+N-1)!}{q! (N-1)!},     (1)


\displaystyle S = Nk \ln{\Omega},     (2)

where \Omega is the multiplicity. The remaining quantities of temperature were obtained using a simplified form of the central difference equations for the first order derivative. The respective definitions of temperature and heat capacity are

\displaystyle T = \frac{\partial U}{\partial S},       (3)


\displaystyle C_{V} = \frac{\partial U}{\partial T},           (4)

where U represents the internal energy of the Einstein solid, and S is the entropy. The generalized from of the first order central difference approximation has the form

\displaystyle \frac{dy_{j}}{dx}\approx \frac{y_{j+1}-y_{j-1}}{2h} + \mathcal{O}(h^{2}),  (5)

where \mathcal{O}(h^{2}) represents the higher order terms, in this case, the quadratic, cubic, quartic, and so on, and h is the step size for each iteration. For the final iteration (when q = 100 units), instead of using the central difference approximation, a backward difference approximation was employed since there does not exist data for q = 101 units of energy. The backward difference approximation has the form

\displaystyle \frac{dy_{j}}{dx}\approx \frac{y_{j}-y_{j-1}}{h}+\mathcal{O}(h).     (6)

Table I (Dimensionless Parameters):

Energy q Ω S/k kT/ε C/Nk
0 1 0 0 N/A
1 50 3.912023005 0.27969284 0.121826198
2 1275 7.150701458 0.328336604 0.453606383
3 22100 10.00333289 0.367875021 0.536183525
4 292825 12.58733044 0.402937926 0.593741905
5 3162510 14.96687657 0.43524436 0.637773801
6 28989675 17.18245029 0.465656087 0.673043377
7 231917400 19.26189183 0.494675894 0.702124659
8 1652411475 21.22550156 0.522626028 0.72660015
9 10648873950 23.08871999 0.549726805 0.747522024
10 62828356305 24.86367234 0.576136157 0.765628174
11 3.427E+11 26.56012163 0.601971486 0.781456694
12 1.74206E+12 28.18608885 0.627322615 0.795411957
13 8.30828E+12 29.74827387 0.652259893 0.807805226
14 3.73873E+13 31.25235127 0.676839501 0.818880855
15 1.59519E+14 32.70318415 0.701107048 0.828833859
16 6.48046E+14 34.1049827 0.725100078 0.837822083
17 2.51594E+15 35.4614241 0.748849881 0.845974847
18 9.3649E+15 36.77574496 0.772382808 0.853399232
19 3.35165E+16 38.05081369 0.795721261 0.860184741
20 1.15632E+17 39.28918792 0.818884446 0.866406816
21 3.8544E+17 40.49316072 0.84188895 0.872129523
22 1.24392E+18 41.66479814 0.864749193 0.877407641
23 3.89401E+18 42.80597005 0.887477794 0.882288296
24 1.18443E+19 43.91837566 0.910085848 0.88681226
25 3.5059E+19 45.00356493 0.932583169 0.891014994
26 1.01132E+20 46.0629565 0.954978471 0.89492748
27 2.84667E+20 47.09785298 0.977279528 0.898576916
28 7.82835E+20 48.10945389 0.999493303 0.901987268
29 2.10556E+21 49.09886689 1.021626052 0.905179739
30 5.54463E+21 50.06711736 1.043683421 0.908173155
31 1.43087E+22 51.01515679 1.065670516 0.910984284
32 3.6219E+22 51.94387004 1.087591972 0.913628113
33 8.99987E+22 52.85408172 1.109452006 0.916118071
34 2.19703E+23 53.74656181 1.131254467 0.918466228
35 5.27286E+23 54.62203054 1.153002873 0.920683463
36 1.24498E+24 55.48116286 1.174700452 0.9227796
37 2.89374E+24 56.32459225 1.196350168 0.924763536
38 6.62514E+24 57.1529142 1.217954752 0.926643346
39 1.4949E+25 57.96668937 1.239516722 0.928426373
40 3.32616E+25 58.76644629 1.261038406 0.930119309
41 7.30133E+25 59.55268389 1.282521958 0.931728264
42 1.58195E+26 60.32587378 1.303969378 0.933258829
43 3.38465E+26 61.08646224 1.325382522 0.934716125
44 7.15391E+26 61.8348721 1.346763119 0.936104855
45 1.49437E+27 62.57150439 1.368112778 0.937429341
46 3.08621E+27 63.29673989 1.389433002 0.938693566
47 6.30374E+27 64.01094048 1.410725193 0.9399012
48 1.27388E+28 64.71445045 1.431990666 0.941055635
49 2.54776E+28 65.40759763 1.45323065 0.942160007
50 5.04457E+28 66.09069447 1.4744463 0.943217222
51 9.89131E+28 66.76403902 1.495638697 0.944229971
52 1.9212E+29 67.42791582 1.516808861 0.945200757
53 3.6974E+29 68.08259672 1.537957749 0.946131903
54 7.05244E+29 68.72834166 1.559086264 0.947025573
55 1.33355E+30 69.36539938 1.580195257 0.947883783
56 2.50041E+30 69.99400804 1.60128553 0.948708411
57 4.64989E+30 70.61439586 1.622357843 0.949501213
58 8.57824E+30 71.22678169 1.643412912 0.95026383
59 1.57025E+31 71.83137547 1.664451416 0.950997796
60 2.85263E+31 72.42837879 1.685473998 0.951704548
61 5.14408E+31 73.01798529 1.706481267 0.952385432
62 9.20957E+31 73.60038111 1.7274738 0.953041713
63 1.63726E+32 74.17574525 1.748452147 0.953674575
64 2.89078E+32 74.74424999 1.769416829 0.954285135
65 5.06999E+32 75.30606117 1.79036834 0.95487444
66 8.83407E+32 75.86133855 1.811307153 0.955443478
67 1.52948E+33 76.41023613 1.832233716 0.955993177
68 2.63161E+33 76.95290236 1.853148456 0.956524415
69 4.50043E+33 77.48948048 1.874051781 0.957038017
70 7.65073E+33 78.02010873 1.894944079 0.957534764
71 1.29308E+34 78.54492059 1.915825721 0.958015394
72 2.17309E+34 79.06404502 1.936697061 0.958480604
73 3.63175E+34 79.57760662 1.957558438 0.958931052
74 6.03655E+34 80.08572588 1.978410175 0.959367363
75 9.98043E+34 80.58851934 1.999252581 0.959790129
76 1.64152E+35 81.08609973 2.020085953 0.960199908
77 2.68612E+35 81.57857622 2.040910573 0.960597234
78 4.37356E+35 82.06605448 2.061726714 0.960982609
79 7.08627E+35 82.54863689 2.082534635 0.961356514
80 1.14266E+36 83.02642266 2.103334587 0.961719401
81 1.8339E+36 83.49950796 2.124126809 0.962071705
82 2.92977E+36 83.96798603 2.14491153 0.962413835
83 4.65939E+36 84.43194735 2.165688971 0.962746183
84 7.37736E+36 84.89147968 2.186459344 0.963069122
85 1.16302E+37 85.34666822 2.207222854 0.963383006
86 1.82567E+37 85.7975957 2.227979695 0.963688173
87 2.85392E+37 86.24434247 2.248730056 0.963984945
88 4.44304E+37 86.68698658 2.269474119 0.964273631
89 6.8892E+37 87.12560389 2.290212057 0.964554522
90 1.064E+38 87.56026816 2.31094404 0.9648279
91 1.63692E+38 87.99105107 2.331670228 0.965094032
92 2.50876E+38 88.41802239 2.352390779 0.965353173
93 3.83058E+38 88.84124995 2.373105841 0.965605568
94 5.82737E+38 89.2607998 2.39381556 0.965851451
95 8.83307E+38 89.67673621 2.414520077 0.966091045
96 1.33416E+39 90.08912176 2.435219526 0.966324564
97 2.00812E+39 90.4980174 2.455914037 0.966552213
98 3.01218E+39 90.90348251 2.476603736 0.966774189
99 4.50306E+39 91.30557493 2.497288745 1.287457337
100 6.70955E+39 91.70435105 2.507672727 1.926043463

 Graphing the entropy vs. energy, and the heat capacity vs. temperature gives the following:

Graphs I & II



Part II: Let q = 100 units and let N = 5000. Using this in the calculation yields the following table for this Einstein solid. This “dilutes” the system and lowers the temperature:

Table II (Dimensionless  Parameters):

Energy q Ω S/k kT/ε C/Nk
0 1 0 0 N/A
1 5000 8.517193 0.122388 0.003049
2 12502500 16.34144 0.131206 0.026553
3 2.08E+10 23.76042 0.137453 0.035575
4 2.61E+13 30.89192 0.14245 0.043342
5 2.61E+16 37.80047 0.146681 0.050387
6 2.18E+19 44.52691 0.150388 0.056922
7 1.56E+22 51.09939 0.153709 0.063064
8 9.74E+24 57.53854 0.156731 0.068885
9 5.42E+27 63.86011 0.159515 0.074436
10 2.72E+30 70.07651 0.162105 0.079755
11 1.24E+33 76.19781 0.164531 0.08487
12 5.16E+35 82.23229 0.166818 0.089804
13 1.99E+38 88.18693 0.168985 0.094575
14 7.13E+40 94.06767 0.171047 0.099198
15 2.38E+43 99.87961 0.173017 0.103687
16 7.47E+45 105.6272 0.174905 0.108052
17 2.2E+48 111.3144 0.176719 0.112303
18 6.14E+50 116.9446 0.178467 0.116448
19 1.62E+53 122.5209 0.180154 0.120494
20 4.07E+55 128.0462 0.181787 0.124447
21 9.73E+57 133.5229 0.183368 0.128314
22 2.22E+60 138.9532 0.184904 0.132098
23 4.85E+62 144.3393 0.186396 0.135806
24 1.02E+65 149.683 0.187849 0.13944
25 2.04E+67 154.9861 0.189265 0.143004
26 3.94E+69 160.2502 0.190646 0.146503
27 7.34E+71 165.4768 0.191995 0.149939
28 1.32E+74 170.6671 0.193314 0.153316
29 2.28E+76 175.8226 0.194604 0.156635
30 3.83E+78 180.9444 0.195868 0.159899
31 6.21E+80 186.0336 0.197106 0.16311
32 9.77E+82 191.0912 0.19832 0.166272
33 1.49E+85 196.1183 0.199512 0.169384
34 2.21E+87 201.1157 0.200682 0.172451
35 3.17E+89 206.0843 0.201831 0.175472
36 4.44E+91 211.025 0.202961 0.17845
37 6.04E+93 215.9384 0.204073 0.181387
38 8E+95 220.8254 0.205166 0.184283
39 1.03E+98 225.6866 0.206243 0.18714
40 1.3E+100 230.5227 0.207304 0.18996
41 1.6E+102 235.3343 0.208349 0.192743
42 1.9E+104 240.122 0.209379 0.195491
43 2.3E+106 244.8863 0.210395 0.198205
44 2.6E+108 249.6279 0.211397 0.200885
45 2.9E+110 254.3472 0.212386 0.203533
46 3.2E+112 259.0447 0.213363 0.20615
47 3.4E+114 263.7209 0.214327 0.208736
48 3.6E+116 268.3762 0.215279 0.211293
49 3.7E+118 273.0112 0.21622 0.213821
50 3.7E+120 277.6261 0.21715 0.21632
51 3.7E+122 282.2214 0.218069 0.218793
52 3.6E+124 286.7975 0.218978 0.221238
53 3.4E+126 291.3548 0.219877 0.223658
54 3.2E+128 295.8935 0.220766 0.226052
55 2.9E+130 300.4141 0.221646 0.228422
56 2.7E+132 304.9169 0.222517 0.230768
57 2.4E+134 309.4022 0.22338 0.23309
58 2.1E+136 313.8703 0.224233 0.235388
59 1.8E+138 318.3214 0.225079 0.237665
60 1.5E+140 322.756 0.225917 0.239919
61 1.2E+142 327.1743 0.226746 0.242152
62 1E+144 331.5765 0.227568 0.244363
63 8.1E+145 335.9628 0.228383 0.246555
64 6.4E+147 340.3337 0.229191 0.248725
65 5E+149 344.6892 0.229991 0.250876
66 3.8E+151 349.0296 0.230785 0.253008
67 2.9E+153 353.3553 0.231572 0.255121
68 2.2E+155 357.6663 0.232353 0.257215
69 1.6E+157 361.9629 0.233127 0.259291
70 1.1E+159 366.2453 0.233896 0.261349
71 8.2E+160 370.5137 0.234658 0.263389
72 5.8E+162 374.7683 0.235414 0.265413
73 4E+164 379.0093 0.236165 0.267419
74 2.7E+166 383.237 0.23691 0.269409
75 1.9E+168 387.4514 0.23765 0.271382
76 1.2E+170 391.6527 0.238384 0.273339
77 8.2E+171 395.8412 0.239113 0.275281
78 5.3E+173 400.0169 0.239837 0.277207
79 3.4E+175 404.1802 0.240556 0.279118
80 2.2E+177 408.331 0.24127 0.281015
81 1.4E+179 412.4696 0.24198 0.282896
82 8.4E+180 416.5962 0.242684 0.284763
83 5.2E+182 420.7108 0.243384 0.286616
84 3.1E+184 424.8136 0.24408 0.288455
85 1.9E+186 428.9048 0.244771 0.29028
86 1.1E+188 432.9845 0.245458 0.292092
87 6.5E+189 437.0529 0.24614 0.29389
88 3.7E+191 441.11 0.246819 0.295676
89 2.1E+193 445.156 0.247493 0.297448
90 1.2E+195 449.191 0.248164 0.299208
91 6.7E+196 453.2152 0.24883 0.300955
92 3.7E+198 457.2286 0.249493 0.30269
93 2E+200 461.2315 0.250152 0.304413
94 1.1E+202 465.2238 0.250807 0.306124
95 5.9E+203 469.2057 0.251458 0.307823
96 3.2E+205 473.1774 0.252106 0.30951
97 1.7E+207 477.1389 0.252751 0.311187
98 8.6E+208 481.0903 0.253392 0.312851
99 4.4E+210 485.0318 0.254029 0.418439
100 2.3E+212 488.9634 0.254345 0.628243


Thus the graphs of the entropy vs. energy and heat capacity vs. temperature follow:


Figure 2. Graphs III and IV.



Figure 3. (Figure 1.14 of Schroeder’s Thermal Physics) Heat Capacity curves for Lead (Pb), Aluminum (Al), and Diamond, respectively as a function of temperature in Kelvin.


Graph II shows the prediction for heat capacity as a function of temperature of an Einstein solid for which there are 100 units of energy and 50 oscillators. The data exhibits a trend that appears to reach an asymptote quickly, then when the temperature reaches T ≈ 2.5, there is a sudden increase in the value of the heat capacity. The approach to determining the final data points was switched from a central difference approximation to a backward difference approximation of the last two entries corresponding to energies q = 99 and q = 100 units. If we ignore the last two, the curve approaches an asymptote at CV = 1. However, the graphs produced are of the dimensionless quantities involved. The overall curve appears to be logarithmic and resembles the heat capacity curve for lead. The initial increase is almost immediate and its slope appears to be slightly less than lead but greater than aluminum.

Graphs III and IV show the prediction for heat capacity in terms of temperature of an Einstein solid for which the energy is the same, but the number of oscillators is now 5000. The temperature has been reduced and the heat capacity vs. temperature yields a graph that shows a trendline that appears linear. Comparing to Figure 3(Fig. 1.14 in the text), this graph resembles the heat capacity curve for diamond. In Figure 3, the diamond curve is linear throughout. The only discrepancies among Graph IV and Figure 3 are the final two data points in Graph IV. Again, a backward difference approximation was used to determine the final data points for this Einstein solid as well.  The value for the constant ε was determined by finding the quotient of the entropy and temperature columns and taking the average value of ε for each energy.


This was the numerical analysis of an Einstein solid’s temperature, energy, entropy, and heat capacity. In the next post, I shall discuss the analytical version of this analysis.

A “Proof” of the Sturm-Liouville Theorem/Problem

IMAGE CREDIT: NASA/JPL: This shows Jupiter’s Great Red Spot; a storm that has been occurring for over 300 years now. Quite recently, however, observations show that the Spot appears to be shrinking in size. 

About a week ago, I was looking through my notebooks and came across an unfinished problem posed by one of my professors. Unfortunately, I was not able to solve the problem during the semester. However, I thought it might be something interesting to consider. I did a quick search and found that the problem he gave us was to prove the Sturm-Liouville Theorem.

The main brute-force method to analytically solving a given partial differential equation is the separation of variables. This method is heavily used by physicists and in doing so transforms the initial boundary value problem (IBVP) into a Sturm-Liouville problem in which we have an ordinary differential equation and linear homogeneous boundary conditions.

Continue reading A “Proof” of the Sturm-Liouville Theorem/Problem

A Narrow, Technical Problem in Partial Differential Equations

While I was in school, one of my professors set this problem to me and my classmates and challenged us to solve it over the next few days. I found the challenge intriguing and it fascinated me, so I thought it was worth sharing. The problem was this:

Show that

\displaystyle v(x,t) = \int_{-\infty}^{\infty} f(x-y,t)g(y)dy,    (1.1)

where \displaystyle g(y) has finite support and also satisfies the PDE

\displaystyle \frac{\partial v}{\partial t} = -\kappa \frac{\partial^{2}v}{\partial x^{2}}.   (1.2)

First off, what does finite support mean? Mathematically speaking, a function has support which is characterized by a subset of its domain whose members do not map to zero, and yet are finite. (Just as a quick note: much of the proper definitions require an understanding in mathematical analysis and measure theory, something which I have not studied in detail, so take that explanation with a grain of salt.)

As for the solution, we can rewrite the given PDE as

\displaystyle \frac{\partial v}{\partial t} - \kappa \frac{\partial^{2}v}{\partial x^{2}} = 0.    (2)

The PDE requires a first-order time derivative and a second-order spatial derivative.

\displaystyle \therefore \frac{\partial v}{\partial t} = \frac{\partial}{\partial t}\int_{-\infty}^{\infty} f(x-y,t)g(y)dy,   (3.1)


\displaystyle \frac{\partial^{2} v}{\partial x^{2}} = \frac{\partial^{2}}{\partial x^{2}}\int_{-\infty}^{\infty} f(x-y,t)g(y)dy.    (3.2)

Next, we substitute Eqs. (3.1) and (3.2) into Eq.(2), yielding

\displaystyle \frac{\partial}{\partial t}\int_{-\infty}^{\infty} f(x-y,t)g(y)dy -\kappa \frac{\partial^{2}}{\partial x^{2}}\int_{-\infty}^{\infty} f(x-y,t)g(y)dy = 0.    (4)

Note that taking the derivative of a function and then integrating that function is equivalent to integrating the function and differentiating the same function, in conjunction with the fact that the sum or difference of the integrals is the integral of the sum or difference (proofs of these facts are typically covered in a course in real analysis). Taking advantage of these gives

\displaystyle \int_{-\infty}^{\infty} \bigg\{\frac{\partial}{\partial t}f(x-y,t)-\kappa\frac{\partial^{2}}{\partial x^{2}}f(x-y,t)\bigg\}g(y)dy = 0.   (5)

Notice that the terms contained in the brackets equate to \displaystyle 0. This means that

\displaystyle \int_{-\infty}^{\infty} 0 \cdot g(y)dy = 0.   (6)

This implies that the function \displaystyle v(x,t) does satisfy the given PDE (Eq.(2)).


Definition of Support in Mathematics: https://en.wikipedia.org/wiki/Support_(mathematics)

Observing the Variable Star W Ursae Majoris

While I was an undergraduate, one of my smaller research projects involved observing the variable star W Ursae Majoris.

In general, there are six types of binary star systems: Optical double, Visual binary, Astrometric binary, Eclipsing binary, Spectrum binary, and Spectroscopic binary.

In this project, my classmate and I were interested in the eclipsing binary (EW) W Ursae Majoris. An eclipsing binary is a binary system in which one of the stars will pass in front of its companion, effectively causing an eclipse. We are able to observe this by way of generating the light curves of the system. An example light curve is shown below:

Image result for eclipsing binary light curve

(Image was obtained at the URL:  https://imagine.gsfc.nasa.gov/educators/hera_college/binary-model.html)

The graph shows a plot of intensity over time (which in this case is an orbital period). Observations of an EW should show dips in the intensity of the two stars. What is really fascinating to me is that we can gain valuable information from this graph. For example, the length of a dip can indicate the masses of the star. If we have a star of mass m_{1} and the other is m_{2} such that m_{2}>m_{1}, and if the duration of the decrease in intensity of the system is significant we can then infer that the mass passing in front of its companion is that of m_{1}. By default, the mass that is being “eclipsed” is m_{2}. Conversely, if the intensity decreases but only for a short while, the positions are reversed, with m_{2} passing in front (relatively speaking) and m_{1} is being “eclipsed”. (I am assuming that the barycenter (i.e. the system’s center of mass) is equidistant from the centers of the two stars.)

Another form of classification of binary stars is whether or not the binary system components are touching or not. More precisely, there are three kinds of close binaries: detached, semi-detached, and contact binary. There are sub-categories of contact binaries: near contact, contact, overcontact,  and double contact.

An equipotential surface map of a system (assuming that the binary system has a mass ratio of 2:1, which may be incorrect as most W UMa binaries have a mass ratio of 10:1) is shown below:

Related image

Image Credit: Fig.1 of Terrell, D., Eclipsing Binary Stars: Past, Present, and Future. JAAVSO Vol. 30, 2001.

To quickly elaborate, each type of contact binary will fill its inner Lagrangian surface (aka Roche lobes) to an extent. In the context of our project, W Ursae Majoris is an overcontact eclipsing binary system.  This type of binary will overfill its inner Lagrangian surface. As a result of this, processes such as mass transfer and accretion can occur. The diagram below shows the orbital evolution of a W UMa EW AC Bootis (in addition to being its own binary system, W UMa is also a class of close binaries)

Image result for overcontact binary roche lobe diagram

Image Credit: Fig. 15 of Alton, K., A Unified Roche-Model Light Curve Solution for the W UMa Binary AC Bootis. JAAVSO. Vol. 38, 2010.

The objective of the project was to image the eclipsing binary, measure the apparent magnitude, to process the images, and to obtain a light curve. To observe this system, a classmate and I made use of the 20″ Ritchey-Chrétien telescope at the university observatory. We made use of the CCD camera attached and set a sequence of images to be taken every two minutes. W UMa has a period of approximately 8 hours, however, due to time constraints (and as much as I would have liked to, the weather was not conducive for observations exceeding two hours), we ended up only taking images for around two hours.

After the session was over, we ended up taking a total of roughly 40-50 images. Additionally, the software used to capture the images simultaneously measured the magnitude of W UMa at the time each image was taken. This allowed us to use Excel (and later on MATLAB) to obtain a partial light curve. However, since this is a partial light curve, we can say that an eclipse (and a short one at that) occurs, yet we cannot determine whether or not the local minimum depicted in the graph below is a primary or a secondary minimum–we simply do not have enough data.

EW UMa light curve

In addition to the partial light curve above, we were able to process the images (using Registax v.6). Below is a stacked image of W UMa. The big blob near the center of the image is the binary. The binary is not able to be resolved by telescopes component-wise.





Caroll, B.W., and Ostlie, D. A., Introduction to Modern Astrophysics. 2017. Cambridge University Press. 7.

Catalog and Atlas of Eclipsing Binaries (CALEB): Types of Binary Stars


American Association of Variable Star Observers (AAVSO) URL: https://www.aavso.org/vsots_wuma

Journal of American Association of Variable Star Observers: Figure References


Coordinates and Transformations of Coordinates

SOURCES FOR CONTENT:  Neuenschwander, D.E., Tensor Calculus for Physics, 2015. Johns Hopkins University Press. 

In this post, I continue the introduction of tensor calculus by discussing coordinates and coordinate transformations as applied to relativity theory. (A side note: I have acquired Misner, Thorne, and Wheeler’s Gravitation and will be using it sparingly given its reputation and weight of the material.)

Continue reading Coordinates and Transformations of Coordinates

Derivation of the Finite-Difference Equations

In my final semester, my course load included a graduate course that had two modules: astronomical instrumentation and numerical modeling. The latter focused on developing the equations of motion of geophysical fluid dynamics (See Research in Magnetohydrodynamics). Such equations are then converted into an algorithm based on a specific type of numerical method of solving the exact differential equation.

The purpose of this post is to derive the finite-difference equations. Specifically, I will be deriving the forward, backward, centered first order equations. We start with the Taylor expansion about the points x_{0}= \pm h:

\displaystyle f(x+h)=\sum_{n=1}^{\infty}\frac{h^{n}}{n!}\frac{d^{n}f}{dx^{n}}, (1)


\displaystyle f(x-h)=\sum_{n=1}^{\infty}(-1)^{n}\frac{h^{n}}{n!}\frac{d^{n}f}{dx^{n}}. (2)

Let f(x_{j})=f_{j}, f(x_{j}+h)=f_{j+1}, f(x_{j}-h)=f_{j-1}. Therefore, if we consider the following differences…

\displaystyle f_{j+1}-f_{j}=f^{\prime}_{j}+f^{\prime \prime}_{j}\frac{h^{2}}{2!}+...+f^{n}_{j}\frac{h^{n}}{n!}, (3)


\displaystyle f_{j}-f_{j-1}=hf^{\prime}_{j}-\frac{h^{2}}{2!}f^{\prime \prime}_{j}+...\mp \frac{h^{n}}{n!}f^{n}_{j}, (4)


\displaystyle f_{j+1}-f_{j-1}=2hf^{\prime}_{j}+\frac{2h^{3}}{3!}f^{\prime\prime\prime}_{j}+..\mp \frac{h^{n}}{n!}f^{n}_{j}, (5)

and if we keep only linear terms, we get

\displaystyle f^{\prime}_{j}=\frac{f_{j+1}-f_{j}}{h}+\mathcal{O}(h), (6)

\displaystyle f^{\prime}_{j}=\frac{f_{j}-f_{j-1}}{h}+\mathcal{O}(h), (7)


\displaystyle f^{\prime}_{j}=\frac{f_{j+1}-f_{j-1}}{2h}+\mathcal{O}(h)

where the first is the forward difference, the second is the backward difference, the last is the centered difference, and \mathcal{O}(h) represents the quadratic, cubic, quartic,quintic,etc. terms. One can use similar logic to derive the second-order finite-difference equations.


Helicity and Magnetic Helicity

SOURCE FOR CONTENT: Davidson, P.A., 2001, An Introduction to Magnetohydrodynamics. 3. 

The purpose of this post is to introduce the concepts of helicity as an integral invariant and its magnetic analog. More specifically, I will be showing that

\displaystyle h = \int_{V_{\omega}}\textbf{u}\cdot \omega d^{3}r  (1)

is invariant and thus correlates to the conservation of vortex line topology using the approach given in the source material above.  I will then discuss magnetic helicity and how it relates to MHD.

Consider the total derivative of \textbf{u}\cdot \omega:

\displaystyle \frac{D}{Dt}(\textbf{u}\cdot \omega) = \frac{D\textbf{u}}{Dt}\cdot \omega + \frac{D\omega}{Dt}\cdot \textbf{u}, (2)

where I have used the product rule for derivatives. Also I denote a total derivative as \frac{D}{Dt} = \frac{\partial}{\partial t}+(\nabla \cdot \textbf{v}) so as not to confuse it with the notation for an ordinary derivative. Recall the Navier-Stokes’ equation and the vorticity equation

\displaystyle \frac{\partial \textbf{u}}{\partial t}+(\nabla \cdot \textbf{v})\textbf{u}=\textbf{F}-\frac{1}{\rho}\nabla P + \nu \nabla^{2}\textbf{u}, (3)


\displaystyle \frac{\partial \omega}{\partial t}+(\nabla \cdot \textbf{v})\omega =(\nabla \cdot \omega)\textbf{u} +\eta \nabla^{2}\omega (4).

Let \textbf{F}=0 and let \eta \nabla^{2} \omega \equiv 0 and \nu \nabla^{2} \textbf{u} \equiv 0. Then,

\displaystyle \frac{D\textbf{u}}{Dt}=-\nabla \frac{P}{\rho},  (5)


\displaystyle \frac{D\omega}{Dt}=(\nabla \cdot \omega)\textbf{u}. (6)

If we substitute Eqs. (5) and (6) into Eq.(2), we get

\displaystyle \frac{D}{Dt}(\textbf{u}\cdot \omega)=-\nabla \frac{P}{\rho}\cdot \omega + (\nabla \cdot \omega)\textbf{u}\cdot \textbf{u}. (7)

Evaluating each scalar product gives

\displaystyle -2\nabla \frac{P}{\rho}\omega + \omega u^{2}\nabla = 0. (8)

If we divide by 2 and note the definition of the scalar product, we may rewrite this as follows

\displaystyle \nabla \cdot \bigg\{-\frac{P}{\rho}\omega +\frac{u^{2}}{2}\omega\bigg\}=0. (9)

With this in mind, consider now the total derivative of (\textbf{u}\cdot \omega) multiplied by a volume element \delta V given by

\displaystyle \frac{D}{Dt}(\textbf{u}\cdot \omega)\delta V = \nabla \cdot \bigg\{-\frac{P}{\rho}\omega +\frac{u^{2}}{2}\omega\bigg\}\delta V. (10)

When the above equation is integrated over a volume V_{\omega}, the total derivative becomes an ordinary time derivative. Thus, Eq.(10) becomes

\displaystyle \int_{V_{\omega}}\frac{d}{dt}(\textbf{u}\cdot \omega)dV=\iint_{S_{\omega}}\bigg\{\nabla \cdot [-\frac{P}{\rho}\omega + \frac{u^{2}}{2}\omega ]\bigg\}\cdot d\textbf{S}=0. (11)

Hence, Eq.(11) shows that helicity is an integral invariant. In this analysis, we have assumed that the fluid is inviscid. If one considers two vortex lines (as Davidson does as a mathematical example), one finds that there exists two contributions to the overall helicity, corresponding to an individual vortex line. If one considers the vorticity multiplied by a volume element, one finds that for each line, their value helicity is the same. Thus, because they are the same, we can say that the overall helicity remains invariant and hence are linked. If they weren’t linked, then the helicity would be 0. In regards to magnetic helicity we define this as

\displaystyle h_{m}=\int_{V_{B}}\textbf{A}\cdot \textbf{B}dV, (12)

where \textbf{A} is the magnetic vector potential which is described by the following relations

\displaystyle \nabla \times \textbf{A}=\textbf{B}, (13.1)


\displaystyle \nabla \cdot \textbf{A}=0. (13.2)

 Again, we see that magnetic helicity is also conserved. However, this conservation arises by means of Alfven’s theorem of flux freezing in which the magnetic field lines manage to preserve their topology.

Method of Successive Approximations-Elementary Methods to Solving Integral Equations

SOURCES: Tricomi, F.G., Integral Equations. 1985. Dover. 1. 

Wazwaz, A.M., A First Course in Integral Equations. 2015. 3.6

In regards to solving integral equations, there are two main types of equations: Volterra and Fredholm equations. The first of which is the topic of this post. First, a brief introduction. Just as there are equations which deal with unknown functions and their derivatives, there also exists another type of equation which involves the same function but include the integral of this function. There are also a class of equations called integro-differential equations (see my series on Monte Carlo and Radiative transfer), in which the equation deals with the function, its derivative, and its integral.

Here, I will be dealing with Volterra integral equations. More specifically, I will be considering the Volterra equation of the second kind (VESK) of the form:

\displaystyle \alpha(x)=h(x)+\lambda\int_{0}^{x}K(x,y)\alpha(y)dy, (1)

where y\in [0,x]. The first term of the integrand K(x,y) denotes the kernel. The kernel of the integral arises from its conversion from an initial value problem. Indeed, solving the integral equation is equivalent to solving the initial value problem of a differential equation. The integral equation includes the initial conditions instead of being added in near the end of the solution of an IVP.

The fact that we are dealing with Volterra equations means that the kernel is subject to the condition:

\displaystyle K(x.y); y > x.

The typical way to solve this involves the method of successive approximations (some call this method Picard’s method of successive approximation). I first came across this method whilst taking my differential equations course. The context in which this arose was that of the existence and uniqueness of solutions to first order differential equations.

The method is as follows:

Suppose we have the equation given in (1). We then define an initial function \alpha_{0}(x)=h(x). Then the next iteration of \alpha is

\displaystyle \alpha_{1}(x)=h(x)+\lambda\int_{0}^{x}K(x,y)h(y)dy. (2)

Naturally, the next term \alpha_{2}(x) is

\displaystyle \alpha_{2}(x)=h(x)+\lambda\int_{0}^{x}K(x,y)f(y)dy+\lambda^{2}\int_{0}^{x}K(x,z)dz \int_{0}^{z}K(z,y)f(y)dy, (3)

or more simply

\displaystyle \alpha_{2}(x)=h(x)+\lambda\int_{0}^{x}K(x,y)\alpha_{1}(y)dy. (4)

The reason I chose to include Eq.(3) is because while reading about this method I found that the traditional expression (Eq.(4)) left much to be desired. For me, it didn’t demonstrate the “successive” part of successive approximations. In general, we may become convinced that

\displaystyle \alpha_{n}(x)=h(x)+\lambda\int_{0}^{x}K(x,y)\alpha_{n-1}(y)dy. (5)

Once the general expression for \alpha_{n}(x) is determined, we may determine the exact solution \alpha(x) via

\displaystyle \alpha(x)=\lim_{n\rightarrow \infty}\alpha_{n}(x). (6)

This is probably one of the simpler methods of solving integral equations, since we do not require any real analysis, but we can obtain solutions for simple integral equations. I will discuss a few other methods of solution in future posts in this series.

“Proof” of Alfven’s Theorem of Flux Freezing

SOURCE FOR CONTENT: Choudhuri, A.R., 2010. Astrophysics for Physicists. Ch. 8. 

In the previous post we saw the consequences of different regimes of the magnetic Reynolds’ number under which either diffusion or advection of the magnetic field dominates. In this post, I shall be doing a “proof” of Alven’s Theorem of Flux Freezing. (I hesitate to call it a proof since it lacks the mathematical rigor that one associates with a proof.) Also note in this post, I will be working with the assumption of a high magnetic Reynolds number.

Alfven’s Theorem of Flux Freezing: Suppose we have a surface S located within a plasma at some initial time t_{0}. From the theorem it is known that the flux of the associated magnetic field is linked with surface S by

\displaystyle \int_{S}\textbf{B}\cdot d\textbf{S}. (1)

At some later time t^{\prime}, the elements of plasma contained within S at t_{0} move to some other point and will constitute some different surface M. The magnetic flux, linked to M at t^{\prime} by

\displaystyle \int_{M}\textbf{B}\cdot d\textbf{M}, (2)

from which we may mathematically state the theorem as

\displaystyle \int_{S}\textbf{B}\cdot d\textbf{S}=\int_{M}\textbf{B}\cdot d\textbf{M}. (3)

If we know that the magnetic field evolves in time in accordance to the induction equation we may express Eq.(3) as

\displaystyle \frac{d}{dt}\int_{S}\textbf{B}\cdot d\textbf{S}=0. (4)

To confirm that this is true, we note the two ways magnetic flux may change as being due to either (1) some intrinsic variability of the magnetic field strength or (2) movement of the surface. Therefore, either way it follows that

\displaystyle \frac{d}{dt}\int_{S}\textbf{B}\cdot d\textbf{S}=\int_{S}\frac{\partial \textbf{B}}{\partial t}\cdot d\textbf{S}+\int_{S}\textbf{B}\cdot \frac{d}{dt}(d\textbf{S}). (5)

Now, consider again the two surfaces. Let us suppose now that M is displaced some distance relative to S. Further, let us also suppose that this displacement occurs during a time interval t^{\prime}=t_{0}+\delta t. Additionally, if we imagine a cylinder formed by projecting a circular cross-section from one surface to the other, we may consider its length to be \delta l with area given by the cross product: -\delta t \textbf{v}\times \delta\textbf{l}. Moreover, since we know that the area of integration is a closed region we see that the integral vanishes (goes to 0). Thus, we may write the difference

\displaystyle d\textbf{M}-d\textbf{S}-\delta \oint \textbf{v}\times \delta \textbf{l}=0 (6).

Recall the definition for a derivative, we may apply it to the second term on the right hand side of Eq.(5) to get

\displaystyle \frac{d}{dt}(d\textbf{S})=\lim_{\delta t\rightarrow 0}\frac{d\textbf{M}-d\textbf{S}}{\delta t}=\oint \textbf{v}\times \delta \textbf{l}. (7)

Thus the term becomes

\displaystyle \int_{S}\textbf{B}\cdot \frac{d}{dt}(d\textbf{S})=\int \oint \textbf{B}\cdot (\textbf{v}\times \delta\textbf{l})=\int \oint (\textbf{B}\times \textbf{v})\cdot \delta l. (8)

Since the integrals that exist interior to the boundary of the surface (call it path C) vanish and recall Stokes’ theorem

\displaystyle \oint_{\partial \Omega}\textbf{F}\cdot d\textbf{r}=\int\int_{\Omega} (\nabla \times \textbf{F})\cdot d\Omega,

and applying it to Eq.(8) we arrive at

\displaystyle \frac{d}{dt}\int_{S}\textbf{B}\cdot d\textbf{S}=\oint_{\partial S}(\textbf{B}\times\textbf{v}) \cdot \delta \textbf{l}=\int\int_{S}\bigg\{\nabla \times (\textbf{B}\times\textbf{v})\bigg\}\cdot d\textbf{S}. (9)

Recall that we are dealing with high magnetic Reynolds number, if we use the corresponding form of the induction equation in Eq.(9) we arrive at

\displaystyle \frac{d}{dt}\int_{S}\textbf{B}\cdot d\textbf{S}=\int\int_{S}d\textbf{S}\cdot \bigg\{ \frac{\partial \textbf{B}}{\partial t}-\nabla\times (\textbf{v}\times \textbf{B})\bigg\}= 0. (10)

Thus, this completes the “proof”.

Deriving the Bessel Function of the First Kind for Zeroth Order

NOTE: I verified the solution using the following text: Boyce, W. and DiPrima, R. Elementary Differential Equations. 

In this post, I shall be deriving the Bessel function of the first kind for the zeroth order Bessel differential equation. Bessel’s equation is encountered when solving differential equations in cylindrical coordinates and is of the form

\displaystyle x^{2}\frac{d^{2}y}{dx^{2}}+x\frac{dy}{dx}+(x^{2}-\nu^{2})y(x)=0, (1)

where \nu = 0 describes the order zero of Bessel’s equation. I shall be making use of the assumption

\displaystyle y(x)=\sum_{j=0}^{\infty}a_{j}x^{j+r}, (2)

where upon taking the first and second order derivatives gives us

\displaystyle \frac{dy}{dx}=\sum_{j=0}^{\infty}(j+r)a_{j}x^{j+r-1}, (3)


\displaystyle \frac{d^{2}y}{dx^{2}}=\sum_{j=0}^{\infty}(j+r)(j+r-1)a_{j}x^{j+r-2}. (4)

Substitution into Eq.(1) and noting the order of the equation we arrive at

\displaystyle x^{2}\sum_{j=0}^{\infty}(j+r)(j+r-1)a_{j}x^{j+r-2}+x\sum_{j=0}^{\infty}(j+r)a_{j}x^{j+r-1}+x^{2}\sum_{j=0}^{\infty}a_{j}x^{j+r}=0. (5)

Distribution and simplification of Eq.(5) yields

\displaystyle \sum_{j=0}^{\infty}\bigg\{(j+r)(j+r-1)+(j+r)\bigg\}a_{j}x^{j+r}+\sum_{j=0}^{\infty}a_{j}x^{j+r+2}=0. (6)

If we evaluate the terms in which j=0 and j=1, we get the following

\displaystyle a_{0}\bigg\{r(r-1)+r\bigg\}x^{r}+a_{1}\bigg\{(1+r)r+(1+r)\bigg\}x^{r+1}+\sum_{j=2}^{\infty}\bigg\{[(j+r)(j+r-1)+(j+r)]a_{j}+a_{j-2}\bigg\}x^{j+r}=0, (7)

where I have introduced the dummy variable m=(j+r)-2 and I have shifted the indices downward by 2. Consider now the indicial equation (coefficients of a_{0}x^{r}),

\displaystyle r(r-1)+r=0, (8)

which upon solving gives r=r_{1}=r_{2}=0. We may determine the recurrence relation from summation terms from which we get

\displaystyle a_{j}(r)=\frac{-a_{j-2}(r)}{[(j+r)(j+r-1)+(j+r)]}=\frac{-a_{j-2}(r)}{(j+r)^{2}}. (9)

To determine J_{0}(x) we let r=0 in which case the recurrence relation becomes

\displaystyle a_{j}=\frac{-a_{j-2}}{j^{2}}, (10)

where j=2,4,6,.... Thus we have

\displaystyle J_{0}(x)=a_{0}x^{0}+a_{1}x+...  (11)

The only way the second term above is 0 is if a_{1}=0. So, the successive terms are a_{3},a_{5},a_{7},..., = 0. Let j=2k, where k\in \mathbb{Z}^{+}, then the recurrence relation is again modified to

\displaystyle a_{2k}=\frac{-a_{2k-2}}{(2k)^{2}}. (12)

 In general, for any value of k, one finds the expression

\displaystyle ... \frac{(-1)^{k}a_{0}x^{2k}}{2^{2k}(k!)^{2}}. (13)

Thus our solution for the Bessel function of the first kind is

\displaystyle J_{0}(x)=a_{0}\bigg\{1+\sum_{k=1}^{\infty}\frac{(-1)^{k}x^{2k}}{2^{2k}(k!)^{2}}\bigg\}. (14)